Control of brain states in patients requiring anesthesia care is a critical topic in modern medicine. E. N. Brown, R. Lydic, and N. D. Schiff, “General anesthesia, sleep, and coma,” New England Journal of Medicine, vol. 363, no. 27, pp. 2638-2650, 2010; P. L. Purdon, E. T. Pierce, E. A. Mukamel, M. J. Prerau, J. L. Walsh, K. F. K. Wong, A. F. Salazar-Gomez, P. G. Harrell, A. L. Sampson, A. Cimenser, et al., “Electroencephalogram signatures of loss and recovery of consciousness from propofol,” Proceedings of the National Academy of Sciences, vol. 110, no. 12, pp. E1142-E1151, 2013; A. O. Rossetti, M. D. Reichhart, M.-D. Schaller, P.-A. Despland, and J. Bogousslavsky, “Propofol treatment of refractory status epilepticus: a study of 31 episodes,” Epilepsia, vol. 45, no. 7, pp. 757-763, 2004; P. Doyle and B. Matta, “Burst suppression or isoelectric encephalogram for cerebral protection: evidence from metabolic suppression studies.” British journal of anaesthesia, vol. 83, no. 4, pp. 580-584, 1999. Today the state of anesthesia is induced manually by continuously administering an anesthetic drug, such as propofol. Anesthesiologists or intensive care unit (ICU) staff monitor indirect measures of the brain's anesthetic state, such as heart rate and blood pressure, and in some cases also the brain's activity on the electroencephalogram (EEG). P. L. Purdon, E. T. Pierce, E. A. Mukamel, M. J. Prerau, J. L. Walsh, K. F. K. Wong, A. F. Salazar-Gomez, P. G. Harrell, A. L. Sampson, A. Cimenser, et al., “Electroencephalogram signatures of loss and recovery of consciousness from propofol,” Proceedings of the National Academy of Sciences, vol. 110, no. 12, pp. E1142-E1151, 2013. They then manually titrate the anesthetic drug infusion rate to maintain a target anesthetic state.
An alternative approach to manual administration is to define numerically a target level of the brain's anesthetic state, and implement a computer-controlled closed-loop anesthetic delivery (CLAD) system or a brain-machine interface (BMI) that automatically monitors the brain's anesthetic state based on the neural activity and adjusts the drug infusion rate in real time to maintain the specified target level. Such automatic control could lead to more reliable and accurate real-time anesthetic delivery than is realistic to expect using manual administration. M. M. Struys, T. De Smet, L. F. Versichelen, S. Van de Velde, R. Van den Broecke, and E. P. Mortier, “Comparison of closed-loop controlled administration of propofol using bispectral index as the controlled variable versus “standard practice” controlled administration,” Anesthesiology, vol. 95, no. 1, pp. 6-17, 2001; J. Agarwal, G. Puri, and P. Mathew, “Comparison of closed loop vs. manual administration of propofol using the bispectral index in cardiac surgery,” Acta anaesthesiologica Scandinavica, vol. 53, no. 3, pp. 390-397, 2009; T. De Smet, M. M. Struys, M. M. Neckebroek, K. Van den Hauwe, S. Bonte, and E. P. Mortier, “The accuracy and clinical feasibility of a new bayesian-based closed-loop control system for propofol administration using the bispectral index as a controlled variable,” Anesthesia & Analgesia, vol. 107, no. 4, pp. 1200-1210, 2008; T. M. Hemmerling, S. Charabati, C. Zaouter, C. Minardi, and P. A. Mathieu, “A randomized controlled trial demonstrates that a novel closed-loop propofol system performs better hypnosis control than manual administration,” Canadian Journal of Anesthesia/Journal canadien d{grave over ( )}anesth{acute over ( )}esie, vol. 57, no. 8, pp. 725-735, 2010; N. Liu, T. Chazot, A. Genty, A. Landais, A. Restoux, K. McGee, P.-A. Lalo{umlaut over ( )} e, B. Trillat, L. Barvais, and M. Fischler, “Titration of propofol for anesthetic induction and maintenance guided by the bispectral index: closed-loop versus manual control: a prospective, randomized, multicenter study.” Anesthesiology, vol. 104, no. 4, pp. 686-695, 2006; N. Liu, T. Chazot, B. Trillat, M. Michel-Cherqui, J. Y. Marandon, J.-D. Law-Koune, B. Rives, M. Fischler, F. L. T. Group, et al., “Closed-loop control of consciousness during lung transplantation: an observational study,” Journal of cardiothoracic and vascular anesthesia, vol. 22, no. 4, pp. 611-615, 2008. Moreover, an automatic system would result in more efficient use of the anesthesia care personnel. This is especially important in medically-induced coma, also termed medical coma, which needs to be maintained for long periods of hours or days. Hence the focus in our recent work has been on developing a BMI for medically-induced coma in a rodent model. M. M. Shanechi, J. J. Chemali, M. Liberman, K. Solt, and E. N. Brown, “A brain-machine interface for control of medically-induced coma,” PLoS computational biology, vol. 9, no. 10, p. e1003284, 2013.
Medically-induced coma is a drug-induced state of profound brain inactivation used after traumatic brain injuries and for treatment of status epilepticus (i.e., uncontrollable seizures). The EEG signal in medical coma, termed burst suppression, consists of bursts of electrical activity alternating with suppression periods. F. Amzica, “Basic physiology of burst-suppression,” Epilepsia, vol. 50, no. s12, pp. 38-39, 2009; S. Ching, P. L. Purdon, S. Vijayan, N. J. Kopell, and E. N. Brown, “A neurophysiological-metabolic model for burst suppression,” Proceedings of the National Academy of Sciences, vol. 109, no. 8, pp. 3095-3100, 2012. For burst suppression, CLAD systems using non-model based control have been implemented in a rodent model (P. Vijn and J. Sneyd, “Iv anaesthesia and eeg burst suppression in rats: bolus injections and closed-loop infusions.” British journal of anaesthesia, vol. 81, no. 3, pp. 415-421, 1998; J. F. Cotten, R. Le Ge, N. Banacos, E. Pejo, S. S. Husain, J. H. Williams, and D. E. Raines, “Closed-loop continuous infusions of etomidate and etomidate analogs in rats: a comparative study of dosing and the impact on adrenocortical function,” Anesthesiology, vol. 115, no. 4, p. 764, 2011) that controlled a constant level of burst suppression rather than time-varying levels needed in medical coma.
Model-based CLADs for management of medical coma only appeared recently in our work (M. M. Shanechi, J. J. Chemali, M. Liberman, K. Solt, and E. N. Brown, “A brain-machine interface for control of medically-induced coma,” PLoS computational biology, vol. 9, no. 10, p. e1003284, 2013) and in (S. Ching, M. Y. Liberman, J. J. Chemali, M. B. Westover, J. D. Kenny, K. Solt, P. L. Purdon, and E. N. Brown, “Real-time closed-loop control in a rodent model of medically induced coma using burst suppression,” Anesthesiology, vol. 119, no. 4, pp. 848-860, 2013.); these CLADs worked by controlling the burst suppression probability (BSP)—taking values in [0;1]—, which is defined as the brain's instantaneous probability of being suppressed and measures the level of burst suppression. M. M. Shanechi, J. J. Chemali, M. Liberman, K. Solt, and E. N. Brown, “A brain-machine interface for control of medically-induced coma,” PLoS computational biology, vol. 9, no. 10, p. e1003284, 2013; S. Ching, M. Y. Liberman, J. J. Chemali, M. B. Westover, J. D. Kenny, K. Solt, P. L. Purdon, and E. N. Brown, “Real-time closed-loop control in a rodent model of medically induced coma using burst suppression,” Anesthesiology, vol. 119, no. 4, pp. 848-860, 2013; J. Chemali, S. Ching, P. L. Purdon, K. Solt, and E. N. Brown, “Burst suppression probability algorithms: state-space methods for tracking eeg burst suppression,” Journal of neural engineering, vol. 10, no. 5, p. 056017, 2013.
The recent CLADs for medical coma have four limiting features that hinder their clinical viability as follows.
(1) Recent CLADs for medical coma require a separate offline system-identification session to be performed before real-time control and treatment can initiate. In this session, a bolus of propofol in the form of a square pulse (M. M. Shanechi, J. J. Chemali, M. Liberman, K. Solt, and E. N. Brown, “A brain-machine interface for control of medically-induced coma,” PLoS computational biology, vol. 9, no. 10, p. e1003284, 2013; S. Ching, M. Y. Liberman, J. J. Chemali, M. B. Westover, J. D. Kenny, K. Solt, P. L. Purdon, and E. N. Brown, “Real-time closed-loop control in a rodent model of medically induced coma using burst suppression,” Anesthesiology, vol. 119, no. 4, pp. 848-860, 2013) is administered to the subject. Recent simulation studies have replaced this square pulse with a ramp-drop pulse (M. B. Westover, S.-E. Kim, S. Ching, P. L. Purdon, and E. N. Brown, “Robust control of burst suppression for medical coma,” Journal of Neural Engineering, vol. 12, no. 4, p. 046004, 2015) that requires as long as 30 min then calculated in this session and used to fit the model parameters. However, such a system-identification session requires a possibly long delay or interruption in treatment, which may not be feasible in the ICU or safe for the patient. Moreover, it may lead to seizure recurrence in status-epilepticus in some patients. M. B. Westover, S.-E. Kim, S. Ching, P. L. Purdon, and E. N. Brown, “Robust control of burst suppression for medical coma,” Journal of Neural Engineering, vol. 12, no. 4, p. 046004, 2015.
(2) Biased performance can occur in prior CLADs for medical coma; for example, in our prior rodent experiments (M. M. Shanechi, J. J. Chemali, M. Liberman, K. Solt, and E. N. Brown, “A brain-machine interface for control of medically-induced coma,” PLoS computational biology, vol. 9, no. 10, p. e1003284, 2013), while control at some levels was unbiased, it exhibited bias at other levels. This is because these CLADs build parametric models of burst suppression and drug dynamics, but assume that model parameters are time-invariant and not a function of the anesthetic level. However, brain dynamics in response to anesthetics are non-stationary and time-varying and may change as a function of the depth of anesthesia. While incorporating loop-shaping can help with relatively small inter-subject variabilities (M. B. Westover, S.-E. Kim, S. Ching, P. L. Purdon, and E. N. Brown, “Robust control of burst suppression for medical coma,” Journal of Neural Engineering, vol. 12, no. 4, p. 046004, 2015), it cannot track the time-varying nature of the biological system.
(3) Prior CLADs for medical coma do not enforce a limit on infusion rate variations at steady state. This may cause over-sensitivity to noise, leading to periodic or unstable operation of the infusion pump as we observed in some of our rodent experiments.
(4) None of the existing CLAD systems provide theoretical guarantees on performance or bias.
(5) Prior CLADs in the anesthesia field have not been generalizable to different anesthetic states. We develop the novel adaptive system to be generalizable to other states of anesthesia in addition to medical coma. We accomplish generalizability by offering a systematic scheme to build parametric models including stochastic models, pharmacokinetic models, pharmacodynamics models.